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We consider the problem of building the relationship of high-energy electron flux between Geostationary Earth Orbit (GEO) and Medium Earth Orbit (MEO). A time-series decomposition technique is first applied to the original data, resulting in trend and detrended part for both GEO and MEO data. Then we predict MEO trend with GEO data using three machine learning models: Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP). Experiment shows that RF gains best performance in all scenarios. Feature extraction analysis demonstrates that the inclusion of lagged features and (possible) ahead features is substantially helpful to the prediction. At last, an application of imputing missing values for MEO data is presented, in which RF model with selected features is used to handle the trend part while a moving block method is for the detrended part.

A dry gas seal (DGS) is one of the key basic components of natural gas transmission pipeline compressors, and the sealing performance of a DGS dealing with complex multi-component pipeline natural gas is different from that dealing with conventional nitrogen medium. In this paper, a spiral groove DGS of the compressor in natural gas transmission pipeline systems is taken as the research object. The thermal hydrodynamic lubrication model of the DGS is established considering turbulence effect and choking effect. Based on the finite difference method, the temperature and pressure distributions and the steady-state performance of the DGS are obtained by simulating. The influence of unitary impurity compositions such as light hydrocarbon, heavy hydrocarbon, non-hydrocarbon, and their contents on the steady-state performance of the DGS is analyzed. The steady-state performance of the DGS dealing with multi-impurity natural gas such as in the West-East gas transmission is investigated under different operating conditions. The results show that turbulence had a significant effect on the DGS, while choking had a weak effect. Increasing the content of light hydrocarbon such as C2H6 and heavy hydrocarbon such as C5H10 resulted in an increase in the gas film stiffness, leakage rate, and the temperature difference between the inlet and outlet, while non-hydrocarbon, such as N2, reduced the temperature difference between the inlet and outlet. The greatest impact on seal performance was produced by the heavy hydrocarbon, followed by the light hydrocarbon, and the least was produced by the non-hydrocarbon. [ABSTRACT FROM AUTHOR]

Coal mine electrical equipments can realize bluetooth communication function of low power consumption by integrating bluetooth low energy module(BLE). However, in the process of integrated design, power protection circuit and battery capacity should be designed according to energy consumption characteristics of BLE module. For the problem, under different power supply voltage and radio frequency power, high-side current monitoring chip ZXCT1010 was adopted to analyze energy consumption characteristics of BLE module under different working states such as reset, initialization, broadcasting and connection. The results indicate that energy consumption of BLE module in radio frequency(RF) transceiving is high, and RF transceiving duration is short, while energy consumption of BLE module in RF static state is low, and RF static state duration is long. Energy consumption of BLE module is less affected by change of RF power, but is greatly affected by change of power supply voltage. The higher the power supply voltage is, the higher the working current and energy consumption of BLE module in each running state will be. Therefore, suitable power supply voltage should be provided and RF power should not be limited in integration application of BLE module.

Non-typhoidal Salmonella (NTS) is a foodborne pathogen and a prevalent causative agent for disease outbreaks globally. The Salmonella enterica serovar 4,[5],12:i:- (S.4,[5],12:i:-) belongs to the monophasic variant of Salmonella typhimurium, which is of current global concern. In this study, the epidemiology and genomic characterization of S. 4,[5],12:i:- isolates from 17 livestock farms in Hunan Province between 2019 and 2020, as well as their susceptibility to 14 antimicrobial agents, were profiled. Twelve Salmonella serotypes were identified using the White–Kauffmann–Le Minor scheme, and whole-genome sequencing analyses were conducted based on these isolates. Overall, 107 Salmonella strains were isolated, of which 73% (78/107) were multidrug resistant. Resistance to tetracycline (85.05%) was found to be the most prevalent, followed by the oqxAB and aac(6′)-Ib-cr genes. S. typhimurium (monophasic) 4,[5],12:i:- was the most common serotype, followed by S. typhimurium and S. derby. Most antimicrobial-resistant strains were isolated from pigs, indicating that they could be important reservoirs of resistant non-typhoidal Salmonella strains. The presence of similar genetic environments in S. 4,[5],12:i:- indicates both vertical and horizontal transmission of resistance plasmids, which may promote the spread of drug resistance genes. Appropriate measures should be taken to curb the prevalence of S. 4,[5],12:i:-. [ABSTRACT FROM AUTHOR]